1. Risk Detection, Operations Efficiency & Economic Analysis Eva K Lee
Georgia Institute of Technology
Atlanta, GA
Problem Statement and Objective:
DHS employ different data sources to perform risk and fraud prediction, with multiple DHS divisions perform risk analysis for routine operations (e.g., CBP, TSA). Effective management of complex data source is essential to risk advances, operational efficiency and resource investment.
Objectives:
Prioritize data sources and data values for reliable prediction
Investigate current gap – both technology and operational – for risk detection
Leverage advance analytic tools to reinforce protection, identify minimum set of data needed, also pinpoints, in the policy level, what sources must be collected and use for best operational performance
Methodology and Data Requirements
Apply machine learning to assess and predict fraud/risk using existing data sources, analyze and measure data quality, and impact on risk/fraud results
Evaluate tradeoff between investing in data improvement and improving effectiveness and efficiency
Simulate system network to explore fraud players, errors in detection and system short/long-term impact
Design game theory model to assess vulnerability of system
Data requirement
Work with DHS to identify current sources used and accessible guidelines
Identify external source date that are useful (to DHS) to acquire
Impact Statement and Relevance to DHS
Roles and Responsibilities
Risk management is a critical component across multiple DHS programs. DHS shall advance in risk assessment ability and adopt technology to improve its ability to detect risk, mitigate risks, improve operations efficiency, and understand the economical impact.
Impact to DHS: improved technological and operational capability and efficiency in risk detection and assessment; improved data usage for risk detection; improved knowledge in ROI (return of investment) in different data stream, ability to prioritize and identify key and reliable risk indicators.
Potential DHS lead: Jerry Booker (or components in CBP)
Director, Risk Management Division
Chief Performance and Enterprise Risk
Timeline and Deliverables
20 days: Complete workplan, identify DHS components
60 days: Complete component visits, finalize collaborators
120 days: Complete review with customers
6 months: Obtain data source information, DHS usage and procedures
9 months: Establish gap and data quality analysis
12 months: Complete machine learning on quality of prediction
15 months: Complete tradeoff analysis on data sources that improve detection performance, ROI analysis
18 months: Report fraud players, systems impact, mitigation strategies
21 months: Complete vulnerability assessment
24 months: Report findings, prioritize actions for improvement